Object removal using lidar-based classification

ABSTRACT

In scenarios involving the capturing of an environment, it may be desirable to remove temporary objects (e.g., vehicles depicted in captured images of a street) in furtherance of individual privacy and/or an unobstructed rendering of the environment. However, techniques involving the evaluation of visual images to identify and remove objects may be imprecise, e.g., failing to identify and remove some objects while incorrectly omitting portions of the images that do not depict such objects. However, such capturing scenarios often involve capturing a lidar point cloud, which may identify the presence and shapes of objects with higher precision. The lidar data may also enable a movement classification of respective objects differentiating moving and stationary objects, which may facilitate an accurate removal of the objects from the rendering of the environment (e.g., identifying the object in a first image may guide the identification of the object in sequentially adjacent images).

BACKGROUND

Within the field of computing, many scenarios involve the capturing andrendering of a representation of an environment, such as a portion of astreet, the interior of a room, or a clearing in a natural setting. As afirst example, a set of images may be captured by a spherical lenscamera and stitched together to form a visual rendering. As a secondexample, the geometry of objects within the environment may be detectedand evaluated in order to render a three-dimensional reconstruction ofthe environment.

In these and other scenarios, the portions of the capturing of theenvironment may be occluded by objects that are present within theenvironment. For example, a capturing of a set of images depicting thesetting and buildings along a street may be occluded by objects such asvehicles, pedestrians, animals, and street signs. While such objects maybe present in the scene in a static or transient manner, it may beundesirable to present such objects as part of the scene. Therefore, insuch scenarios, image processing techniques may be utilized to detectthe portions of the respective images depicting such objects and toremove such objects from the rendering of the environment. For example,image recognition techniques may be applied to the respective images toidentify the presence and location of depicted objects such as vehiclesand people (e.g., based on a visual estimation of the size, shape, andcolor of the objects, utilizing imaging properties such as scale,shadowing, and parallax), and to refrain from including those portionsof the images in the rendering of the environment.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

While the removal of occluding objects from a rendering of anenvironment may be desirable, it may be difficult to achieve the removalthrough image processing techniques, due to the limitations in theprecision of image processing techniques. For example, automated imagetechniques for identifying the presence of individuals in an image maybe skewed by properties such as visual distortion, glare, and shadows,and may therefore result in false negatives (e.g., failing to identify apresent individual, and rendering the depiction of part or all of theindividual into the scene) and/or false positives (e.g., incorrectlyidentifying a portion of an image as depicting an individual, andtherefore removing the individual from the image).

However, in some scenarios, laser imaging (“lidar”) data may beaccessible that provides a supplementary set of information about theobjects present in an environment. For example, some image capturingvehicles are also equipped with a lidar emitter that emits alow-powered, visible-spectrum laser at a specific wavelength, and alidar detector that detects light at the specific wavelengthrepresenting a reflection off of nearby objects. The resulting “lidarpoint cloud” is often utilized, e.g., for navigation and/or calibrationof the vehicle and cameras. However, lidar data may also be capable ofidentifying the objects present in the environment, and, morespecifically, classifying the respective objects according to a movementclassification (e.g., moving, foreground stationary, backgroundstationary, and fixed-ground stationary). These types of objectidentification and movement classification may guide the omission of theobjects from the rendering of the environment. For example, identifyingan object in a first image of an environment, using the lidar data andmovement classification, may facilitate the identification of the sameobject in sequentially adjacent images in an image sequence of theenvironment (e.g., images chronologically preceding and following thefirst image).

To the accomplishment of the foregoing and related ends, the followingdescription and annexed drawings set forth certain illustrative aspectsand implementations. These are indicative of but a few of the variousways in which one or more aspects may be employed. Other aspects,advantages, and novel features of the disclosure will become apparentfrom the following detailed description when considered in conjunctionwith the annexed drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary scenario featuring a vehiclemoving within an environment while capturing images of the environmentand other objects present in the environment.

FIG. 2 is an illustration of an exemplary scenario featuring a capturingof a lidar point cloud of an environment around a vehicle and depictingthe other objects present within the environment.

FIG. 3 is an illustration of an exemplary scenario featuring anevaluation of a lidar point cloud over time to classify identifiedobjects as stationary or moving in accordance with the techniquespresented herein.

FIG. 4 is an illustration of an exemplary scenario featuring a renderingof an environment with an omission of objects detected by the evaluationof lidar data in accordance with the techniques presented herein.

FIG. 5 is a flow diagram of an exemplary method of evaluating a lidarpoint cloud over time to classify identified objects as stationary ormoving in accordance with the techniques presented herein.

FIG. 6 is a component block diagram of an exemplary system configured toevaluate a lidar point cloud over time to classify identified objects asstationary or moving in accordance with the techniques presented herein.

FIG. 7 is an illustration of an exemplary computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

FIG. 8 is an illustration of an exemplary scenario featuring anevaluation of images of an environment captured from differentperspectives utilizing an evaluation of lidar data.

FIG. 9 is an illustration of an exemplary scenario featuring anevaluation of a sequence of images of an environment captured in a timesequence and utilizing an evaluation of lidar data.

FIG. 10 illustrates an exemplary computing environment wherein one ormore of the provisions set forth herein may be implemented.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form in order to facilitatedescribing the claimed subject matter.

A. Introduction

Within the field of machine vision, many scenarios involve an automatedevaluation of images of an environment to detect the objects present inthe environment and depicted in the images, and, more particularly, toidentify the position, size, orientation, velocity, and/or accelerationof the objects. As a first example, the evaluation may involve vehiclesin a transit environment, including automobiles, bicycles, andpedestrians in a roadway as well as signs, trees, and buildings, inorder to facilitate obstacle avoidance. As a second example, a physicalobject tracking system may evaluate the motion of an object within anenvironment in order to interact with it (e.g., to catch a ball or otherthrown object). As a third example, a human actor present in amotion-capture environment may be recorded while performing variousactions in order to render animated personalities with human-likemovement. In various scenarios, the analysis may be performed inrealtime or near-realtime (e.g., to facilitate a device or individual ininteracting with the other present objects), while in other scenarios,the analysis may be performed retrospectively (e.g., to identify themovement of objects that were present at the time of the capturing).These and other scenarios often involve the capturing and evaluation ofa set of visible light images, e.g., with a still or motion camera, andthe application of visual processing techniques to human-viewableimages. For example, machine vision techniques may attempt to evaluate,from the contents of the image, the type, color, size, shape,orientation, position, speed, and acceleration of an object based onvisual cues such as shadowing from light sources, perspective, relativesizes, and parallax effects.

FIG. 1 presents an illustration of an exemplary scenario featuring a setof objects 102 comprising vehicles operating in an environment 100(e.g., with a particular motion vector 104 while operating a camera 106to capture a sequence of images of the environment 100. In thisexemplary scenario, other objects 102 are also present in theenvironment 100, and may involve both objects 102 having a motion vector104 and stationary vehicles 108, such as parked cars. The environment100 may also include other types of moving objects, such as individuals110, as well as various stationary objects, such as signs 112 andbuildings 114. Within such scenarios, a reconstruction of theenvironment 100 may later be performed. As a first example, a set oforthogonal, panoramic, and/or spherical images captured by the camera106 may be stitched together to form a three-dimensional imagereconstruction of the view of the environment 100 from the perspectiveof the vehicle. As a second example, a detection of the position, size,and shape of the objects 102 in the environment 100 may enable athree-dimensional geometric reconstruction of the environment 100.

In these and other scenarios, it may be desirable to remove part or allof the objects detected in the environment 100. As a first example, theobjects 102 may be associated with individuals, and it may be desirableto remove identifying indicators of the individuals who were presentwhen the environment 100 was captured (e.g., by removing an entireobject 102 present in the environment 100, such as a depiction of anindividual 110, and/or by removing only a personally identifying portionof an object 102, such as the face of the individual 110 or a licenseplate of a vehicle). As a second example, it may be desirable togenerate a rendering of the environment 100 that is not obscured by theobjects 102 temporarily present in the environment 100 at the time ofcapturing. As a third example, it may be desirable to depict themovement of the detected objects 102 within the environment 100, whichmay involve generating a static three-dimensional reconstruction of theenvironment 100 omitting all of the objects 102, and then to addanimation of the objects 102 through the environment 100 and/or togenerate a more accurate three-dimensional model of the moving objectsfor various applications, including sharpened visualization, furtherclassification of the object (e.g., identifying the make and model of amoving vehicle), and movement tracking.

However, in these scenarios, the achievable precision in theidentification of the movement of the objects from an inspection ofvisual images may be limited. For example, techniques such asperspective and parallax may provide only general estimates,particularly for objects that are distant from the camera lens, and/ormay be distorted by visual artifacts, such as glare and shadows. As aresult, such evaluative techniques may produce estimates with lowprecision and/or a high degree of error, and may be inadequate forparticular uses. As a first example, the image processing techniques mayfail to recognize some objects 102 or portions thereof (i.e., falsenegatives), and may therefore fail to omit the objects 102 from therendering of the environment 100. As a second example, the imageprocessing techniques may incorrectly identify a portion of an image asdepicting an individual 110 (i.e., false positives), and may omitportions of the rendering of the environment 100 that are not associatedwith objects 102. For example, a visual image may capture a billboarddepiction of a vehicle, or a stone sculpture of an individual. Imageprocessing techniques may incorrectly identify these portions of theenvironment 100 as depicting actual vehicles or individuals 110, and mayremove them from an image-based rendering of the environment 100, thusremoving valuable information about the environment 100 in the absenceof a significant motivation of privacy preservation and/or removal ofobscuring objects 102 within the environment 100 (i.e., it may bedesirable to include these objects 102 as significant features of theenvironment 100).

B. Presented Techniques

Many scenarios involving the evaluation of object movement may beachieved through devices (such as objects 102) that also have access todata from a laser imaging (“lidar”) capturing device, which may emit aset of focused, low-power beams of light of a specified wavelength, andmay detect and record the reflection of such wavelengths of light fromvarious objects. The detected lidar data may be used to generate a lidarpoint cloud, representing the lidar points of light reflected from theobject and returning to the detector, thus indicating specific points ofthe objects present in the environment 100. By capturing and evaluatinglidar data over time, such a device may build up a representation of therelative positions of objects around the lidar detector (e.g., thelocations of other objects 102 with respect to the object 102 operatingthe camera 106). These representations may be used while generatingreconstructions of the environment 100 to omit the depictions of theobjects 102.

FIG. 2 presents an illustration of an exemplary scenario 200 featuringone such technique for capturing an environment 100 including a set ofobjects 102 (e.g., vehicles) using a lidar point cloud. In thisexemplary scenario 200, a first object 102 is positioned behind a movingobject 102 having a motion vector 104, and a stationary vehicle 108having no detectable motion. The first object 102 may comprise a lidaremitter 202 that emits a lidar signal 204 ahead of the first object 102.The lidar reflection 206 of the lidar signal 204 may be detected by alidar detector 208, and captured as a sequence of lidar point clouds 210representing, at respective time points 212, the lidar points 214detected by the lidar detector 208 within the environment 100. Inparticular, the detected lidar points 214 may cluster around particularobjects (such as objects 102), which may enable the lidar detector 208to identify the presence, size, and/or range of the objects atrespective time points 212. Additionally, by comparing the ranges of theobjects 102 or other objects over time, the lidar detector 208 maydetermine an approximate velocity of the objects. For example, whencomparing the lidar point clouds 210 over time, the lidar points 214representing the moving object 102 and the lidar points 214 representingthe stationary vehicle 108 may move with respect to each other and thefirst object 102. However, if the object 102 carrying the lidar detector208 is also moving, the approximate velocities of the objects 102 orother objects represented by the lidar points 214 in the lidar pointcloud 210 may be distorted; e.g., stationary vehicles 108 may appear tobe moving, while moving objects 102 that are moving at an approximatelyequivalent velocity and direction as the object 102 carrying the lidardetector 208 may appear as stationary vehicles 108. Such complicationsmay be come exacerbated if the objects are detected as moving inthree-dimensional space as well as over time, and/or if the orientationof the object 102 carrying the lidar detector 208 also changes (e.g.,accelerating, decelerating, and/or turning). Even determining whetherrespective objects (such as objects 102) are moving or stationary maybecome difficult in view of these factors.

In order to classify respective objects (such as objects 102) as movingor stationary, and optionally in order to identify other properties suchas position and velocity, techniques may be utilized to translate thelidar points 214 of the respective lidar point clouds 210 tothree-dimensional space. FIG. 3 presents an illustration of an exemplaryscenario 300 featuring a translation of a set of lidar point clouds 210to classify the objects depicted therein. In this exemplary scenario300, for respective lidar point clouds 210, the lidar points 214 aremapped 302 to a voxel 306 in a three-dimensional voxel space 304. Next,the voxels 306 of the three-dimensional voxel space 304 may be evaluatedto detect one or more voxel clusters of voxels 306 (e.g., voxels 306that are occupied by one or more lidar points 214 in the lidar pointcloud 210, and that share an adjacency with other occupied voxels 306 ofthe three-dimensional voxel space 304, such as within a specified numberof voxels 306 of another occupied voxel 306), resulting in theidentification 308 of one or more objects 312 within an object space 310corresponding to the three-dimensional voxel space 304. Next, for therespective lidar points 214 in the lidar point cloud 210, the lidarpoint 214 may be associated with a selected object 312. The movement ofthe lidar points 214 may then be classified according to the selectedobject 312 (e.g., the objects may be identified as moving or stationarywith the object 312 in the three-dimensional voxel space 304). Accordingto the classified movements of the lidar points 214 associated with theobject 312 (e.g., added for the object spaces 310 at respective timepoints 212), a projection 314 of the lidar points 214 and an evaluationof the movements of the lidar points 214 associated with respectiveobjects 312, the movement of the respective objects 312 may beclassified. For example, and as depicted in the projection 314 of FIG.3, the lidar points 214 associated with the first object 312, afterprojection in view of the three- dimensional voxel space 304, appear tobe moving with respect to the lidar detector 208, and may result in aclassification 316 of the object 312 as a moving object; while the lidarpoints 214 associated with the second object 312, after projection inview of the three-dimensional voxel space 304, appear to be stationaryafter adjusting for the movement of the lidar detector 208, and mayresult in a classification 316 of the object 312 as a stationary object.

These and other techniques for evaluating a lidar point cloud 210 todetect and classify a set of objects 102 in an environment 100 mayfacilitate the process of generating a rendering of the environment 100omitting such objects 102. FIG. 4 presents an illustration of anexemplary scenario 400 featuring an omission of such objects 102 from arendering 408 of an environment 100. In this exemplary scenario 400, arepresentation of the environment 100 is captured from a captureperspective 402 (e.g., a position within the environment 100), which mayinclude both the environment 100 and the objects 102 present therein,including vehicles, individuals 110, signs 112, and buildings 114. Someobjects (such as the signs 112 and buildings 114) may be regarded aspart of the environment 100 that are to be included in the rendering ofthe environment 100 (e.g., as fixed-ground objects and backgroundobjects), while other objects 102 may be regarded as transients to beremoved from the rendering of the environment 100 (e.g., as movingobjects and stationary foreground objects). Moreover, some objects 102may include only an object portion of the object 102 that is to beomitted. For example, rather than omitting an entire individual 110 orvehicle, it may be desirable to omit only an object portion of theobject 102 that may be associated with a particular individual 110, suchas the individual's face, or a license plate 404 of a vehicle.

In order to generate a rendering 408 of the environment 100 satisfyingthese considerations, the representation of the environment 100,including the lidar point cloud 210 captured by a lidar detector 208,may be evaluated to identify the objects 102 in the environment 100, anda movement classification 316 of such objects 102. A rendering 408 ofthe environment 100 assembled from the capturing 406 (e.g., astitched-together image assembled from a set of panoramic and/orspherical images) may therefore present a spherical view 410 from thecapture perspective 402 that omits any portions of the capturing 406depicting the objects 102 detected within the environment 100 andaccording to the movement classification 316. For example, the rendering408 may exclude all objects 102 that are classified to be moving.Objects 102 that are classified as stationary may further be evaluatedto distinguish stationary foreground objects (e.g., objects 102 that arewithin a particular range of the capture perspective 402) fromfixed-ground objects (such as signs 112) and/or background objects (suchas buildings 114). As a result, the rendering 408 of the environment 100may contain omitted portions 412, e.g., spots in the rendering 408 thathave been blurred, blackened, or replaced with a depiction of theenvironment 100 that is not obscured by an object 102. Additionally, itmay be desirable to omit only an object portion 414 of an object 102,such as the license plate 404 of the vehicle. In this manner, varioustechniques may be applied to utilize a lidar point cloud 210 (including,as but one example, the evaluation of the lidar point cloud 210 in theexemplary scenario 300 of FIG. 3) in the omission of objects 104 in arendering 408 of an environment 100 in view of the classification 316 ofthe objects 312 according to the lidar point cloud 210 in accordancewith the techniques presented herein.

C. Exemplary Embodiments

FIG. 5 presents a first exemplary embodiment of the techniques presentedherein, illustrated as an exemplary method 500 of rendering anenvironment 100 omitting one or more objects 102. The exemplary method500 may be implemented, e.g., as a set of instructions stored in amemory device of the device, such as a memory circuit, a platter of ahard disk drive, a solid-state storage device, or a magnetic or opticaldisc, and organized such that, when executed on a processor of thedevice, cause the device to operate according to the techniquespresented herein. The exemplary method 500 begins at 502 and involvesexecuting 404 the instructions on a processor of the device.Specifically, the instructions are configured to generate 506, for theenvironment 100, a lidar point cloud 210 comprising at least one lidarpoint 214. The instructions are also configured to map 508 respectivelidar points 214 in the lidar point cloud 210 to identify at least oneobject 102 in the environment 100. The instructions are also configuredto select 510 a movement classification 316 of the respective at leastone object 102 according to the lidar points 214. The instructions arealso configured to generate 512 the rendering 408 of the environment 100omitting at least an object portion of the respective at least oneobject 102 according to the movement classification 316 of the object102. In this manner, the exemplary method 500 achieves the rendering 408of the environment 100 omitting at least one object 102 in a manner thatis facilitated by lidar data in accordance with the techniques presentedherein, and so ends at 514.

FIG. 6 presents a second exemplary embodiment of the techniquespresented herein, illustrated as an exemplary system 606 configured torender an environment 100 omitting at least one object 102 of theenvironment 100. The exemplary system 606 may be implemented, e.g., asinstructions stored in a memory component of the device 602 andconfigured to, when executed on a processor 604 of the device 602, causethe device 602 to operate according to the techniques presented herein.The exemplary system 606 includes an object identifier 608 that isconfigured to generate, for the environment 100, a lidar point cloud 210comprising at least one lidar point 214; map the respective lidar points214 in the lidar point cloud 210 to identify at least one object 102 inthe environment 100; and select a movement classification 316 of therespective at least one object 102 according to the lidar points 214,thereby outputting a set of identified objects 612. The exemplary system606 also includes an environment renderer 610, which is configured togenerate the rendering 408 of the environment 100 omitting at least anobject portion of the respective at least one object 102 based on theset of identified objects 612 and the movement classification 316 of theobjects 102, thus producing an environment rendering 614 of theenvironment 100 omitting one or more objects 102 in accordance with thetechniques presented herein.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to apply the techniquespresented herein. Such computer-readable media may include, e.g.,computer-readable storage devices involving a tangible device, such as amemory semiconductor (e.g., a semiconductor utilizing static randomaccess memory (SRAM), dynamic random access memory (DRAM), and/orsynchronous dynamic random access memory (SDRAM) technologies), aplatter of a hard disk drive, a flash memory device, or a magnetic oroptical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set ofcomputer-readable instructions that, when executed by a processor of adevice, cause the device to implement the techniques presented herein.Such computer-readable media may also include (as a class oftechnologies that are distinct from computer-readable storage devices)various types of communications media, such as a signal that may bepropagated through various physical phenomena (e.g., an electromagneticsignal, a sound wave signal, or an optical signal) and in various wiredscenarios (e.g., via an Ethernet or fiber optic cable) and/or wirelessscenarios (e.g., a wireless local area network (WLAN) such as WiFi, apersonal area network (PAN) such as Bluetooth, or a cellular or radionetwork), and which encodes a set of computer-readable instructionsthat, when executed by a processor of a device, cause the device toimplement the techniques presented herein.

An exemplary computer-readable medium that may be devised in these waysis illustrated in FIG. 7, wherein the implementation 700 comprises acomputer-readable storage device 702 (e.g., a CD-R, DVD-R, or a platterof a hard disk drive), on which is encoded computer-readable data 704.This computer-readable data 704 in turn comprises a set of computerinstructions 706 configured to operate according to the principles setforth herein. In one such embodiment, the processor-executableinstructions 706 may be configured to perform a method 708 of renderingan environment 100 omitting a set of objects 102, such as the exemplarymethod 500 of FIG. 5. In another such embodiment, theprocessor-executable instructions 706 may be configured to implement asystem for rendering an environment 100 omitting a set of objects 102,such as the exemplary system 606 of FIG. 6. Some embodiments of thiscomputer- readable medium may comprise a computer-readable storagedevice (e.g., a hard disk drive, an optical disc, or a flash memorydevice) that is configured to store processor-executable instructionsconfigured in this manner. Many such computer-readable media may bedevised by those of ordinary skill in the art that are configured tooperate in accordance with the techniques presented herein.

D. Variations

The techniques discussed herein may be devised with variations in manyaspects, and some variations may present additional advantages and/orreduce disadvantages with respect to other variations of these and othertechniques. Moreover, some variations may be implemented in combination,and some combinations may feature additional advantages and/or reduceddisadvantages through synergistic cooperation. The variations may beincorporated in various embodiments (e.g., the exemplary method 500 ofFIG. 5 and the exemplary system 606 of FIG. 6) to confer individualand/or synergistic advantages upon such embodiments.

D1. Scenarios

A first aspect that may vary among embodiments of these techniquesrelates to the scenarios wherein such techniques may be utilized.

As a first variation of this first aspect, the techniques presentedherein may be utilized to evaluate many types of objects, includingobjects 102 traveling in an environment 100, such as automobiles andbicycles traveling on a roadway or airplanes traveling in an airspace,and individuals moving in an area, such as a motion-capture environment100.

As a second variation of this first aspect, the techniques presentedherein may be utilized with many types of lidar signals 204, includingvisible, near-infrared, or infrared, near-ultraviolet, or ultravioletlight. Various wavelengths of lidar signals 204 may present variousproperties that may be advantageous in different scenarios, such aspassage through various media (e.g., water or air of varying humidity),sensitivity to various forms of interference, and achievable resolution.

As a third variation of this first aspect, the techniques presentedherein may be utilized with various types of lidar emitters 202 and/orlidar detectors 208, such as various types of lasers and photometricdetectors. Additionally, such equipment may be utilized in theperformance of other techniques (e.g., lidar equipment provided forrange detection in vehicle navigation systems may also be suitable forthe classification of moving and stationary objects), and may be appliedto both sets of techniques concurrently or in sequence. Those ofordinary skill in the art may devise a broad variety of such scenariosfor the identification and movement classification 316 of objects 312according to the techniques presented herein.

D2. Object Identification and Classification

A second aspect that may vary among embodiments of these techniquesrelates to the manner of evaluating the lidar point cloud 210 toidentify the objects 102 and the movement classification 316 thereof.

As a first variation of this second aspect, the particular techniquesillustrated in the exemplary scenarios of FIG. 2-3 may be utilized toevaluate the lidar point cloud 210 and to detect and classify objects102 associated with respective lidar points 214. However, it may beappreciated that these exemplary scenarios present only one suchtechnique for evaluating a lidar point cloud 210, and that otherevaluative techniques may be utilized that add to, remove from, and/oralter these techniques. As a first example, the mapping of lidar points214 to objects 102 may involve a mapping 302 to a three-dimensionalvoxel space 304, as illustrated in the exemplary scenario 300 of FIG. 3.Alternatively, such mapping may include a two-dimensional mapping totwo-dimensional voxels 306 (e.g., a two-dimensional grid representing anaerial view of the environment 100), or a proximity calculation thatidentifies clusters of proximate lidar points 214 that appear to movetogether in the environment 100 over time. As a second such example,identifying the movement classification 316 of the objects 102 may bebased on the movement classification of the individual lidar points 214associated with the object 102, such as in the exemplary scenario 300 ofFIG. 3, or may involve calculating an average movement of the lidarpoints 214 associated with the object 102, and/or may involveidentifying regions of the three-dimensional voxel space 304 havinglidar points 214 that appear to be moving in a comparatively similardirection.

As a second variation of this second aspect, the identification and/ormovement classification 316 of objects 102 may be achieved by algorithmsdevised and encoded by humans. Alternatively or additionally, suchidentification may be achieved in whole or in part by a machine-learningtechnique. For example, a device 602 may comprise a movement classifierthat is trained and configured to select a movement classification 316of an object 102 in an environment 100 using the lidar point cloud 210,such as an artificial neural network or a genetically evolved algorithm.The techniques presented herein may involve selecting the movementclassification 316 of the respective objects 102 by invoking themovement classifier.

As a third variation of this second aspect, many techniques may be usedto facilitate the identification of objects 102 in the environment 100along with the evaluation the lidar point cloud 210. As a first suchexample, a device 602 may have access to at least one image of theenvironment 100, and the detection of the objects 102, movementclassification 316 of the objects 102, and/or the rendering of theenvironment by focusing an image portion of the image depicting theobject 102 using the movement classification 316 of the object 102. Forexample, the precise information about the position, orientation, shape,shape, and/or velocity of the object 102 in the environment 100 mayenable the identification of a specific portion of the image that isassociated with the area of the lidar points 214 associated with theobject 102, and thus likely depicting the object 102 in the environment.Such focusing may involve, e.g., trimming the portion of the image tothe boundaries of the object 102 matching the lidar points 214, and/orselecting a focal distance of the image to sharpen the selected portionof the image. As a further variation, the image may be focused on atleast one selected object portion of an object 102, such as an objectportion of the object 102 that may be personally identifying of anindividual 110, such as a face of an individual 110 or a license plateof a vehicle. As one such example, evaluating the lidar point cloud 210may enable a determination of the orientation of the object 102 and adetermination of the position of the object portion of the object 102(e.g., detecting the orientation of a vehicle may enable a deduction ofthe location of the license plate on the vehicle, such as a particularflat rectangle on a bumper of the vehicle, and/or an area at a certainheight above ground level). Alternatively or additionally, theevaluation of the lidar point cloud 210 may enable a focusing of animage portion of the image that depicts the selected object portion ofthe object 102 using the movement classification 316 of the object 102.This type of focusing may enable, e.g., the generation of a rendering408 of the environment 100 omitting the image portion depicting theselected object portion of the object 102.

As a fourth variation of this second aspect, in some scenarios, at leastone object 102 in the environment 100 may be visually associated with atleast one character, such as recognizable letters, numbers, symbols,and/or pictograms. In such scenarios, the identification of objects 102and/or object portions in respective images may involve the recognitionof characters through an optical character recognizer. For example,identifying the object 102 in the environment 100 may further involveapplying an optical character recognizer to one or more images of theenvironment 100 to detect the at least one character, and associatingthe character with the object 102 in the environment 100. This variationmay be advantageous, e.g., for automatically detecting symbols on apersonally identifying license plate of a vehicle, and may be used inconjunction with the evaluation of the lidar point cloud 210.

As a fifth variation of this second aspect, the evaluation of theobjects 102 may include many types of movement classification 316. Forexample, respective objects 102 may be classified as having a movementclassification 316 selected from a movement classification setcomprising a moving object, a stationary foreground object, a stationarybackground object, and a fixed-ground object. These movementclassifications 316 may facilitate determinations in which objects 102to omit from the rendering 408 of the environment 100; e.g., movingobjects and stationary foreground objects may be presumed to betransient with respect to the environment 100 and may be omitted, whilestationary background objects and fixed-ground objects may be presumedto be integral to the environment 100 and may not be omitted.

As a sixth variation of this second aspect, the identification ofobjects 102 in a first capturing 406 of the environment 100 using thelidar point cloud 210 may facilitate the identification of objects 102in a second capturing 406 of the environment 100. FIG. 8 presents afirst exemplary scenario 800 featuring one such facilitatedidentification of objects 102. In this first exemplary scenario 800, anenvironment 100 is captured from a first perspective 802 having a firstviewing angle 804, and may include an object 102 such as a vehicle. Whenthe object 102 has been recognized in the capturing 406 from the firstperspective 802 according to the lidar points 214 of the lidar pointcloud 210, a representation portion 810 of a first environmentrepresentation 808 of the environment 100 from the first perspective 802(e.g., captured concurrently with the detection of the lidar point cloud210 by the lidar detector 208) may be identified as depicting the object102. Additionally, this identification may facilitate an identificationof the object 102 in while evaluating a second environmentrepresentation 808 captured from a second perspective 812 form adifferent viewing angle 804. For example, a device 602 may, uponidentifying the object 102 in the environment 100 from a firstenvironment representation 808, identify a position of the object 102 inthe environment 100 according to the first perspective (e.g.,determining the location of the first perspective 802 with respect tothe environment 100, determining the relative position of the object 102with respect to the lidar detector 208, and deducing the position of theobject 102 with respect to the environment 100. As one such example, theposition of the object 102 with respect to the environment 100 may bedetermined as the location of the voxel cluster of the object 102 in thethree-dimensional voxel space 304. Conversely, the second viewing angle804 of the second perspective 812 may be mapped to a particular sectionof the three-dimensional voxel space 304, and areas of thethree-dimensional voxel space 304 (including the voxel cluster of theobject 102) may be mapped to portions of the second image captured fromthe second perspective 812. In this manner, even before evaluating thesecond image, it may be possible for the device 602 to determine wherein the second image the object 102 is likely to appear. Such variationsmay be utilized, e.g., to guide the identification process (e.g.,focusing the evaluation of the second environment representation 808 ona selected portion); to inform and/or verify an image evaluationprocess; or even to select the environment representation portion 810 ofthe environment representation 808 without applying any image evaluationtechniques.

FIG. 9 presents an illustration of an exemplary scenario 900 featuring asecond application of such variations that may reduce false positivesand false negatives in the identification and/or omission of objects102. In this exemplary scenario 900, an environment 100 is captured froma first perspective 802 and a second perspective 812 at different timepoints 212. However, at the second time point 212, an environmentrepresentation 808 of the environment 100 may be distorted, e.g., by ashadow 902 altering the depiction of the object 102. However, if theenvironment representations 808 present an environment representationsequence (e.g., a sequence of images captured at consistent intervals),the identification of an environment representation portion 810 of afirst object representation 808 from the first perspective 802 mayfacilitate the accurate evaluation of the second environmentrepresentation 808 and the omission of the object 102, as it is adjacentin the environment representation sequence (e.g., a next or precedingimage that varies only by a small time interval) to the firstenvironment representation 808 where the object 102 has been identified.For example, upon identifying an object 102 in the environment 100 froma first environment representation 808 of the environment representationsequence, the device may examine a second environment representation 808that is adjacent to the first environment representation 808 in theenvironment representation sequence to identify the object 102 in thesecond environment representation 808 (e.g., using the same environmentrepresentation portion 810 in each environment representation 808,and/or offsetting the environment representation portion 810 based on amovement vector 104 of the object 102 and/or a perspective delta fromthe first perspective 802 to the second perspective 812, and the timeinterval). That is, if a first position of the object 102 detected inthe first environment representation 808 may be identified and projectedto estimating a second position in the second environment representation808, then the device 602 may initiate and/or focus an examination of thesecond environment representation 808 based on the second position andthe correlated environment representation portion 810 of the secondenvironment representation 808. In this manner, the detection of anobject 102 in one capturing 406 of the environment 100 may inform thedetection of the same object 102 in other capturings 406 of theenvironment 100 varying in time and/or space, thereby facilitating theefficiency and/or accuracy of the object detection. Many such evaluativetechniques may facilitate the identification and movement classification316 of the objects 102 in the environment 100 in accordance with thetechniques presented herein.

D3. Uses of Object Identification and Movement Classification

A third aspect that may vary among embodiments of these techniquesinvolves the uses of the object identification and movementclassification 316 in accordance with the techniques presented herein.

As a first variation of this third aspect, the omission of the objects102 may be in furtherance of various scenarios. As a first such example,the omission of the objects 102 from the rendering 408 of theenvironment 100 may be performed, e.g., to preserve the privacy of theindividuals 110. As a second such example, the omission of the objects102 may be performed to obscure the identity of the environment 100(e.g., removing any object 102 may be distinctively identify theenvironment 100, as compared with any other environment 100 of similarappearance). As a third such example, the omission of the objects 102may be performed to provide a rendering 408 of the environment 100 thatis not obscured by the objects 102 (e.g., generating an “empty”environment 100 as if the objects 102 had not been present), which maybe achievable by substituting an image portion of an image capturing 406of the environment 100 with a second portion of the environment 100 thatcorresponds to the same view, but that is not obscured. As a fourth suchexample, the omission of the objects 102 may enable the insertion ofother objects 102, or even of the same objects 102 at different timepoints; e.g., animation of the objects 102 moving through the rendering408 of the static environment, as a modeled or computer-generateddepiction of such motion, may be more desirable than rendering 408 theenvironment 100 to include the motion of the object 102 captured duringthe capturing 406.

As a second variation of this third aspect, the omission of the objects102 from the rendering 408 of the environment 100 may be achieved invarious ways. As a first such example, for scenarios involving acapturing 406 of at least one image of the environment 100, the objects102 may be omitted by blurring at least an image portion of at least oneimage depicting the at least an object portion of the at least oneobject 102. Alternatively or additionally, the omission may be achievedby blackening or whitening the image portion, or substituting anotherimage portion for the object portion (e.g., pasting an image of a secondobject 102 over the depiction of the first object 102 in the environment100). As another such example, if the object 102 comprises an individual110, and the capturing 406 includes at least one personal identifier(such as a recognizable feature of the individual 110), the device 602may remove at least one of the recognizable features of the individual110 from the rendering 408 of the environment 100. As another example,if the object 102 comprises a vehicle that is associated with anindividual 110, and if the capturing 406 of the environment 100 includesa personal identifier comprising a vehicle identifier attached to thevehicle, the device 602 may remove the vehicle identifier of the vehiclefrom the rendering 408 of the environment 100. As yet another example,the device 602 may have access to at least one background portion of therendering 408 of the environment 100 corresponding to a representationportion of the capturing 406 that has been obscured by an object 102(e.g., a second image of the environment 100 from the same captureperspective 402 that is not obscured by the object 102), and a device602 may replace the obscured portion of the capturing 406 with thebackground portion while generating the rendering 408 of the environment100.

As a third variation of this third aspect, a device 602 may, in additionto omitting the object 102 from the rendering 408 of the environment100, apply the information extracted from the evaluation of the lidarpoint cloud 210 to achieve other features. As a first such example, adevice 602 may, upon receiving a request to generate a second rendering408 of the environment 100 that includes the objects 102, insert theobjects 102 into the rendering 408 of the environment 100 to generatethe second rendering 408. That is, having removed the objects 102 fromthe rendering 408 of the environment 100, the device 602 may fulfill arequest to reinsert the objects 102, e.g., as a differential depictionof a populated vs. empty environment 100. The insertion may also presentdifferent depictions of the objects 102 than the portions of thecapturing 406 removed from the rendering 408, such as stylized,iconified, and/or clarified depictions of the objects 102. As a secondsuch example, a device 602 may, for respective objects 102 that aremoving in the environment 100 according to the movement classification316, estimate a movement vector 104 of the object 102 at one or moretime points 212. Additionally, the device 602 may generate within therendering 408 a depiction of the object 102 moving through theenvironment 100. For example, having extracted a static, emptyrepresentation of the environment 100, insert an animation of the movingobjects 102 to depict action within the environment 100 over time. As athird such example, the information may be used to select an object typeof the respective objects 102 (e.g., the evaluation of the lidar pointcloud 210 may inform and facilitate an object recognition technique).These and other uses of the information generated by the evaluation ofthe lidar point cloud 210 may be devised and applied to a variety ofscenarios by those of ordinary skill in the art in accordance with thetechniques presented herein.

E. Computing Environment

FIG. 10 and the following discussion provide a brief, generaldescription of a suitable computing environment to implement embodimentsof one or more of the provisions set forth herein. The operatingenvironment of FIG. 10 is only one example of a suitable operatingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the operating environment. Examplecomputing devices include, but are not limited to, personal computers,server computers, hand-held or laptop devices, mobile devices (such asmobile phones, Personal Digital Assistants (PDAs), media players, andthe like), multiprocessor systems, consumer electronics, mini computers,mainframe computers, distributed computing environments that include anyof the above systems or devices, and the like.

Although not required, embodiments are described in the general contextof “computer readable instructions” being executed by one or morecomputing devices. Computer readable instructions may be distributed viacomputer readable media (discussed below). Computer readableinstructions may be implemented as program modules, such as functions,objects, Application Programming Interfaces (APIs), data structures, andthe like, that perform particular tasks or implement particular abstractdata types. Typically, the functionality of the computer readableinstructions may be combined or distributed as desired in variousenvironments.

FIG. 10 illustrates an example of a system 1000 comprising a computingdevice 1002 configured to implement one or more embodiments providedherein. In one configuration, computing device 1002 includes at leastone processing unit 1006 and memory 1008. Depending on the exactconfiguration and type of computing device, memory 1008 may be volatile(such as RAM, for example), non-volatile (such as ROM, flash memory,etc., for example) or some combination of the two. This configuration isillustrated in FIG. 10 by dashed line 1004.

In other embodiments, device 1002 may include additional features and/orfunctionality. For example, device 1002 may also include additionalstorage (e.g., removable and/or non-removable) including, but notlimited to, magnetic storage, optical storage, and the like. Suchadditional storage is illustrated in FIG. 10 by storage 1010. In oneembodiment, computer readable instructions to implement one or moreembodiments provided herein may be in storage 1010. Storage 1010 mayalso store other computer readable instructions to implement anoperating system, an application program, and the like. Computerreadable instructions may be loaded in memory 1008 for execution byprocessing unit 1006, for example.

The term “computer readable media” as used herein includescomputer-readable storage devices. Such computer-readable storagedevices may be volatile and/or nonvolatile, removable and/ornon-removable, and may involve various types of physical devices storingcomputer readable instructions or other data. Memory 1008 and storage1010 are examples of computer storage media. Computer-storage storagedevices include, but are not limited to, RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, Digital Versatile Disks (DVDs) orother optical storage, magnetic cassettes, magnetic tape, and magneticdisk storage or other magnetic storage devices.

Device 1002 may also include communication connection(s) 1016 thatallows device 1002 to communicate with other devices. Communicationconnection(s) 1016 may include, but is not limited to, a modem, aNetwork Interface Card (NIC), an integrated network interface, a radiofrequency transmitter/receiver, an infrared port, a USB connection, orother interfaces for connecting computing device 1002 to other computingdevices. Communication connection(s) 1016 may include a wired connectionor a wireless connection. Communication connection(s) 1016 may transmitand/or receive communication media.

The term “computer readable media” may include communication media.Communication media typically embodies computer readable instructions orother data in a “modulated data signal” such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” may include a signal that has one or moreof its characteristics set or changed in such a manner as to encodeinformation in the signal.

Device 1002 may include input device(s) 1014 such as keyboard, mouse,pen, voice input device, touch input device, infrared cameras, videoinput devices, and/or any other input device. Output device(s) 1012 suchas one or more displays, speakers, printers, and/or any other outputdevice may also be included in device 1002. Input device(s) 1014 andoutput device(s) 1012 may be connected to device 1002 via a wiredconnection, wireless connection, or any combination thereof. In oneembodiment, an input device or an output device from another computingdevice may be used as input device(s) 1014 or output device(s) 1012 forcomputing device 1002.

Components of computing device 1002 may be connected by variousinterconnects, such as a bus. Such interconnects may include aPeripheral Component Interconnect (PCI), such as PCI Express, aUniversal Serial Bus (USB), Firewire (IEEE 1394), an optical busstructure, and the like. In another embodiment, components of computingdevice 1002 may be interconnected by a network. For example, memory 1008may be comprised of multiple physical memory units located in differentphysical locations interconnected by a network.

Those skilled in the art will realize that storage devices utilized tostore computer readable instructions may be distributed across anetwork. For example, a computing device 1020 accessible via network1018 may store computer readable instructions to implement one or moreembodiments provided herein. Computing device 1002 may access computingdevice 1020 and download a part or all of the computer readableinstructions for execution. Alternatively, computing device 1002 maydownload pieces of the computer readable instructions, as needed, orsome instructions may be executed at computing device 1002 and some atcomputing device 1020.

F. Usage of Terms

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

As used in this application, the terms “component,” “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, those skilled inthe art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

Various operations of embodiments are provided herein. In oneembodiment, one or more of the operations described may constitutecomputer readable instructions stored on one or more computer readablemedia, which if executed by a computing device, will cause the computingdevice to perform the operations described. The order in which some orall of the operations are described should not be construed as to implythat these operations are necessarily order dependent. Alternativeordering will be appreciated by one skilled in the art having thebenefit of this description. Further, it will be understood that not alloperations are necessarily present in each embodiment provided herein.

Moreover, the word “exemplary” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as advantageousover other aspects or designs. Rather, use of the word exemplary isintended to present concepts in a concrete fashion. As used in thisapplication, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims may generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated exemplary implementations of thedisclosure. In addition, while a particular feature of the disclosuremay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes”, “having”, “has”, “with”, or variants thereof areused in either the detailed description or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

What is claimed is:
 1. A method of generating a rendering of anenvironment including at least one object, the method performed on adevice comprising a processor and comprising: executing on the processorinstructions configured to: generate for the environment a lidar pointcloud comprising at least one lidar point; map respective lidar pointsin the lidar point cloud to identify at least one object in theenvironment; select a movement classification of the respective at leastone object according to the lidar points; and generate the rendering ofthe environment omitting at least a portion of the respective at leastone object according to the movement classification of the object. 2.The method of claim 1: the device comprising a movement classifierconfigured to select a movement classification of an object in anenvironment using the lidar point cloud; and selecting the movementclassification of the respective at least one object comprising:invoking the movement classifier to select the movement classificationof the object in the environment.
 3. The method of claim 1: the devicehaving access to at least one image of the environment; and generatingthe rendering of the environment comprising: for respective images:focusing a portion of the image depicting the object using the movementclassification of the object; and generating the rendering of theenvironment omitting the portion of the image depicting the object. 4.The method of claim 3: respective objects comprising at least oneselected object portion; and generating the rendering of the environmentcomprising: for respective images: focusing an image portion of theimage depicting the selected object portion of the object using themovement classification of the object; and generating the rendering ofthe environment omitting the image portion depicting the selected objectportion of the object.
 5. The method of claim 1: at least one object inthe environment visually associated with at least one character; andidentifying the at least one object in the environment furthercomprising: applying an optical character recognizer to the environmentto detect the at least one character; and associating the at least onecharacter with the object in the environment.
 6. The method of claim 1,selecting the movement classification comprising: for the respective atleast one object, selecting a movement classification of the object froma movement classification set comprising: a moving object; a stationaryforeground object; a stationary background object; and a fixed-groundobject.
 7. The method of claim 1: the device having access to: a firstenvironment representation of the environment captured from a firstperspective, and a second environment representation of the environmentcaptured from a second perspective that is different from the firstperspective; and identifying the at least one object in the environmentfurther comprising: upon identifying an object in the environment fromthe first environment representation: using the first perspective,identifying a position of the object in the environment; identifying anrepresentation portion of the second environment representationcorresponding to the position of the object and the second perspective;and identify the object in the representation portion of the secondenvironment representation.
 8. The method of claim 1: the method furthercomprising: capturing at least one image of the environment; andomitting the at least an object portion of the respective at least oneobject comprising: blurring at least an image portion of at least oneimage depicting the at least one object portion of the at least oneobject.
 9. The method of claim 1: at least one selected objectcomprising a personal identifier of at least one individual associatedwith the object; and omitting the at least an object portion of theselected object comprising: removing from the rendering of theenvironment the at least one personal identifier of the at least oneindividual.
 10. The method of claim 9: the object comprising anindividual; the personal identifier comprising at least one recognizablefeature of the individual; and omitting the at least an object portionof the selected object comprising: removing the at least onerecognizable feature of the individual from the rendering of theenvironment.
 11. The method of claim 9: the object comprising a vehicleassociated with the individual; the personal identifier comprising avehicle identifier attached to the vehicle; and omitting the at least anobject portion of the selected object comprising: removing the vehicleidentifier of the vehicle from the rendering of the environment.
 12. Themethod of claim 1, generating the rendering of the environment furthercomprising: for least one background portion of the rendering of theenvironment that is obscured by a selected object, replacing at leastone object portion of at least one object depicted in the environmentwith a background portion of the environment.
 13. The method of claim 1,the instructions further configured to, upon receiving a request togenerate a second rendering of the environment including the objects,insert the objects into the rendering of the environment to generate thesecond rendering.
 14. The method of claim 1, the instructions furtherconfigured to, for respective objects that are moving in the environmentaccording to the movement classification, generate within the renderinga depiction of the object moving through the environment.
 15. The methodof claim 1, the instructions further configured to, for respectiveobjects that are moving in the environment according to the movementclassification, estimate a movement vector of the object.
 16. The methodof claim 1, the instructions further configured to, for respectiveobjects, select an object type of the object.
 17. The method of claim 1:the device having access to an environment representation sequence ofthe environment; and identifying the at least one object in theenvironment further comprising: upon identifying an object in theenvironment from a first environment representation of the environmentrepresentation sequence, examine a second environment representationthat is adjacent to the first environment representation in theenvironment representation sequence to identify the object in the secondenvironment representation.
 18. The method of claim 17, examining thesecond environment representation further comprising: estimating asecond position of the object in the second environment representationbased on a first position of the object in the first environmentrepresentation; and examine the second position of the secondenvironment representation to identify the object in the secondenvironment representation.
 19. A system for rendering of an environmentincluding at least one object involving a device having a memory and aprocessor, the system comprising: an object identifier configured to:generate for the environment a lidar point cloud comprising at least onelidar point; map respective lidar points in the lidar point cloud toidentify at least one object in the environment; and select a movementclassification of the respective at least one object according to thelidar points; and an environment renderer configured to generate therendering of the environment omitting at least an object portion of therespective at least one object according to the movement classificationof the object.
 20. A computer-readable storage device comprisinginstructions that, when executed on a processor of a device, cause thedevice to generate a rendering of an environment including at least oneobject by: generating for the environment a lidar point cloud comprisingat least one lidar point; mapping respective lidar points in the lidarpoint cloud to identify at least one object in the environment;selecting a movement classification of the respective at least oneobject according to the lidar points; and generating the rendering ofthe environment omitting at least an object portion of the respective atleast one object according to the movement classification of the object.